OpenSearch and CMS: Building Modern Content Search and AI Experiences

Search has always been an important capability for content management systems. But what organizations expect from search has changed dramatically.

Traditional CMS search was relatively straightforward: index pages, match keywords, and return a list of links. Modern digital experiences demand much more. Visitors expect highly relevant search results, faceted navigation, filtering, recommendations, semantic understanding, and increasingly, conversational answers generated by AI.

At the same time, developers need search infrastructure capable of operating across large volumes of structured and unstructured content without turning the CMS database into a bottleneck.

This is where OpenSearch becomes particularly valuable.

OpenSearch provides a distributed search and analytics platform that supports traditional full-text search, filtering, aggregations, vector search, semantic search, hybrid search, and AI-oriented retrieval. Combined with a modern headless CMS, it can become much more than the technology behind a website search box. It becomes a core part of the content delivery architecture.

What Is OpenSearch?

OpenSearch is an open source, distributed search and analytics engine designed for indexing, querying, and analyzing large amounts of data.

For CMS applications, one of its fundamental advantages is that content can be transformed into search documents optimized specifically for retrieval.

Consider a CMS managing thousands of articles, products, documentation pages, locations, videos, and other content types. Each item may contain structured properties such as:

• Title
• Description
• Body content
• Categories and taxonomy
• Author
• Product attributes
• Publication date
• Geography
• Language
• Tags
• Custom metadata

Rather than repeatedly traversing the CMS repository to answer complex queries, relevant content and metadata can be indexed into OpenSearch. Applications can then execute sophisticated queries against that index.

OpenSearch supports traditional lexical search using BM25, along with filtering, relevance tuning, vector search, and AI-powered search techniques.

That separation between content management and content retrieval is an important architectural principle.

The CMS Should Manage Content, Not Pretend to Be a Search Engine

Many CMS platforms historically stored content in relational databases. Search capabilities were subsequently layered onto those databases.

That approach can work for simple websites. It becomes increasingly limiting as digital experiences become more sophisticated.

A content repository and a search index solve fundamentally different problems.

The CMS repository should provide the authoritative representation of content and support capabilities such as content modeling, editing, versioning, workflows, governance, and publishing.

The search engine should optimize content for retrieval.

This distinction becomes particularly important in modern headless architectures, where the same content may power websites, mobile applications, portals, e-commerce experiences, digital signage, customer applications, and AI systems.

Instead of asking the content repository to satisfy every possible runtime query, published content can be indexed into a system specifically designed for search and retrieval.

The resulting architecture looks conceptually like this: CMS -> Publish -> OpenSearch Index -> Content Applications

The CMS remains the system responsible for managing content. OpenSearch becomes a high-performance retrieval layer for experiences consuming that content.

Beyond the Search Box

When developers hear "CMS search," they may naturally think about the search field at the top of a website.

That is only one application. OpenSearch can power product catalogs, knowledge bases, documentation sites, content discovery, related-content components, filtering, navigation, recommendations, personalization, and application APIs.

Its aggregation capabilities, for example, can group and analyze indexed documents across fields and ranges. That makes architectures such as faceted navigation possible.

Imagine an industrial manufacturer managing thousands of products in its CMS. A visitor might search for "high temperature pressure sensor" and then filter results by industry, operating temperature, product family, region, certification, and availability.

The content model provides those structured attributes. OpenSearch makes them rapidly searchable. This illustrates an important relationship between CMS architecture and search quality: better structured content creates better search possibilities.

A modern CMS should therefore make structured content easy to model and publish while allowing specialized search infrastructure to index and retrieve it efficiently.

Headless CMS Makes Search More Important

The rise of headless CMS makes this architecture even more relevant.

In a traditional page-oriented CMS, the primary unit of delivery was often an HTML page. With headless CMS, content is increasingly delivered through APIs. Applications decide how that content is presented.

Search therefore becomes another powerful content API.

An application might request: "Give me the ten most relevant articles about cloud security published during the last year for financial services customers."

Another might request: "Find products in this category compatible with these three specifications."

These are retrieval problems, not simply page-rendering problems.

Search engines such as OpenSearch provide developers with the querying, filtering, ranking, and aggregation capabilities needed to build these experiences.

The Next Step: Semantic and Vector Search

Keyword search remains extremely useful, particularly when users know the terminology contained in the content. But people don't always search using the same words content authors use.

Suppose a documentation site contains an article titled: "Configuring Single Sign-On with SAML"

A user might search: "How do I connect our corporate login?"

Traditional keyword search may struggle if the terminology doesn't overlap sufficiently. Semantic search approaches the problem differently.

Content can be transformed into numerical representations called vector embeddings. Those vectors encode semantic relationships between pieces of content. Search queries can also be converted into vectors, allowing the system to find content that is conceptually similar rather than merely containing the same words.

OpenSearch supports vector indexes and k-nearest-neighbor searches for this purpose, primarily for building AI-enabled experiences.

For CMS platforms, this creates an entirely new generation of content discovery experiences.

Hybrid Search: Keywords and Meaning

Semantic search does not necessarily replace traditional keyword search. The two techniques solve different problems.

Exact terminology can be extremely important when searching for product numbers, technical terms, names, SKUs, regulations, error codes, or specific phrases. Semantic search is particularly valuable when users express an idea differently from the source content.

Modern search architectures can therefore combine both. OpenSearch supports hybrid search, combining lexical keyword search with semantic vector search to improve relevance.

For a CMS-powered digital experience, that means search can consider both:

• What words did the visitor use?, and
  
• What does the visitor appear to mean?

That represents a substantial improvement over the basic CMS search functionality many organizations have relied upon for decades.

OpenSearch, CMS, and Generative AI

The emergence of generative AI makes the CMS/search relationship even more strategically important.

Large language models are extremely capable, but enterprise applications generally cannot rely exclusively on information encoded in a model's training data. They need access to current, authoritative organizational content.

That is the purpose of Retrieval-Augmented Generation (RAG).

A simplified RAG architecture looks like: User Question -> Retrieve Relevant Content -> Provide Context to LLM -> Generate Answer

The retrieval step is critical. OpenSearch can store vector embeddings alongside traditional indexed data and supports use cases including semantic search, hybrid search, recommendation systems, and RAG.

This creates a natural architecture for AI-enabled content platforms: CMS -> Structured Content -> Search/Vector Index -> Retrieval -> LLM

The CMS provides governed, authoritative content. OpenSearch provides retrieval. The LLM provides language understanding and generation.

Each component does what it is designed to do.

Search Is Becoming Infrastructure for AI Agents

The implications go beyond AI chatbots.

AI agents increasingly need to find information before they can reason or act.

Consider an AI agent operating on an enterprise website. A visitor asks: "Which of your products would work for an outdoor installation in temperatures below -20°C?"

The answer may require searching product specifications, filtering products by structured properties, retrieving supporting documentation, and then generating an understandable response.

That is fundamentally a search and retrieval problem before it becomes a generative AI problem. OpenSearch already supports semantic and hybrid retrieval techniques applicable to AI systems, while RAG provides a mechanism for grounding generated responses in retrieved information.

As AI agents become another consumer of enterprise content, the quality and architecture of the content retrieval layer will become increasingly important.

OpenSearch in CrafterCMS

CrafterCMS uses OpenSearch as its backing search engine. It's built in to CrafterCMS, so you don't need to procure/manage a separate search engine/vendor. 

Upon content publishing, the Crafter Deployer service indexes published content and keeps OpenSearch synchronized with content changes, while Crafter Engine uses OpenSearch to execute search queries.

This fits into a broader architectural principle within CrafterCMS: authoring and delivery are truly decoupled.

CrafterCMS does not require delivery nodes to depend on a shared authoring database. Content is published to the delivery tier, where it can be indexed locally for retrieval and used to power dynamic digital experiences. CrafterCMS delivery nodes are designed around a shared-nothing architecture and can operate independently.

That architecture has traditionally provided advantages for scalability, availability, and dynamic content delivery.

It is becoming even more valuable in the AI era. OpenSearch is no longer useful only for finding content containing a particular phrase. The same search infrastructure can support increasingly sophisticated retrieval patterns including vector and hybrid search.

That creates a foundation for building search experiences, recommendations, RAG applications, and AI agents directly around CMS-managed content.

Search Is Now Part of the Content Architecture

For years, search was often treated as an optional CMS feature. Build the website first. Add a search box later.

That model is becoming obsolete.

Modern digital experiences depend upon the ability to retrieve the right content, for the right user, in the right context, at the right time. And increasingly, the consumer retrieving that content won't always be a human navigating a website.

It may be a mobile application. It may be a digital commerce experience. It may be an enterprise assistant. It may be an AI agent.

The CMS remains responsible for creating, structuring, governing, and publishing trusted content. But a modern search platform such as OpenSearch provides the retrieval infrastructure necessary to make that content useful across increasingly sophisticated digital experiences.

The combination is powerful:

• Structured content from the CMS
• High-performance retrieval from OpenSearch
• Semantic understanding through vectors
• Natural-language interaction through AI

Search is no longer simply a feature of the CMS. It is becoming a fundamental layer of the modern content architecture.

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